Automated biological and cytological specimen screeners, such as the AutoPap.RTM. 300 available from NeoPath Inc. of Redmond, Wash., quantitatively evaluate biological and cytological samples. These automated screeners utilize computer based imaging to evaluate biological and cytological samples such as Pap smears. Automated screeners interact with medical experts, such as cytologists and pathologists, by providing useful information to assist the expert in the identification of abnormal cases and in making a final diagnosis. These systems identify potential abnormal specimens and recommend further review by human cytologists and pathologists. The cytologist or pathologist then diagnoses the potential abnormal specimen. The cytologist or pathologist provides the final diagnosis of the potentially abnormal specimen.
An abnormal sub-population comprises those specimens from a set of specimens to be screened that are abnormal. Given a set of specimens comprising normal and abnormal, two methods are commonly used by automated screeners to assist medical experts in identifying the abnormal sub-population.
The first method employs a system that screens a slide and attempts to identify suspicious objects or cells. The system attempts to identify suspicious objects or cells as those that satisfy a set of predetermined criteria of potential abnormality. Then the system acquires images for further review by human experts. The system selects a subset of objects within each specimen based on the object detection results for further image review by the cytologist or pathologist. This first method allows the expert to avoid reviewing the remaining areas of each slide if the review result of the selected images does not reveal any abnormality.
This first method has many disadvantages. For example, reviewers must review many selected images for every specimen even if the specimen is clearly normal by any measurement. Furthermore, image review of the selected images in a significant percentage of cases is inconclusive. Therefore, the reviewer must resort to examining the specimen microscopically. This first method only performs measurement and recognition of individual cells or objects and does not consider factors such as: the contextual information between objects on the same slide, the global characteristics of the slide such as the staining level and the relationship between the slides of a population under screening. This first method does not provide a test for or information regarding specimen sampling adequacy. This first method does not consider the slide to slide variation problem within a slide set to be screened.
The second method computes a slide score or specimen score. The slide or specimen score is defined as a likelihood of abnormality, based on all the quantitative measurements of the slides, including overall cell detection results, contextual information of the specimen and even the patient risk measurement of the specimen, among others. The second method then determines whether the slide should be reviewed by a human expert by applying a predetermined threshold to the score. The threshold is determined by ranking all slide scores and selecting a score value that enables a certain percentage, e.g., 50%, of higher score slides to be selected for review. The second method selects a subset of all the slides based on the scores for further human review. The goal of the selection is to select a subset of slides that contains all the abnormals of the original population. By doing so, slide review of the remaining slides is no longer needed and the remaining slides may be reported as normal.
The second method allows for effective selection of abnormal slides for review based on the slide score. To facilitate human review of the potential abnormal material on a biological specimen, what is needed is information directing a level of review needed to minimize a false negative ratio while lowering cost.
However, information is needed concerning the location of potential abnormal material residing on the specimen. Without location information, human reviewers still need to search the entire slide and may miss critical material detected by the automated device and dismiss the specimen as a false-positive case. By providing location information, a human reviewer may dismiss a normal slide after reviewing the selected "most suspicious areas" and determining the slide to be a "false-positive". The second method directs review by a human reviewer thereby improving the sensitivity of human review and the efficiency of review process. This can significantly reduce the workload of the human reviewer.